run.sh 1.5 KB

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  1. # erase "results/models_comparisons.csv" file and write new header
  2. file_path='results/models_comparisons.csv'
  3. erased=$1
  4. if [ "${erased}" == "Y" ]; then
  5. echo "Previous data file erased..."
  6. rm ${file_path}
  7. mkdir -p models_info
  8. touch ${file_path}
  9. # add of header
  10. echo 'model_name; number_of_approximations; coeff_of_determination;' >> ${file_path}
  11. fi
  12. for feature in {'variances','samples'}; do
  13. for n in {3,4,5,6,7,8,9,10,15,20,25,30}; do
  14. for row in {1,2,3,4,5}; do
  15. for column in {1,2,3,4,5}; do
  16. # Run creation of dataset and train model
  17. DATASET_NAME="data/dataset_${n}_${feature}_column_${column}_row_${row}.csv"
  18. if ! grep -q "${MODEL_NAME}" "${file_path}"; then
  19. echo "Run computation data for model ${MODEL_NAME}"
  20. python generate/make_dataset.py --n ${n} --feature ${feature} --each_row ${row} --each_column ${column}
  21. fi
  22. for model in {"SGD","Ridge"}; do
  23. MODEL_NAME="${n}_${feature}_column_${column}_row_${row}_${model}"
  24. if ! grep -q "${MODEL_NAME}" "${file_path}"; then
  25. echo "Run computation for model ${MODEL_NAME}"
  26. python train_model.py --data ${DATASET_NAME} --model ${model}
  27. else
  28. echo "${MODEL_NAME} results already computed.."
  29. fi
  30. done
  31. done
  32. done
  33. done
  34. done